Determinants of Capital in the
Property and Casualty Insurance Industry
Elena Grubisic*
Darrell Leadbetter**
Abstract
Canadian property and casualty insurance companies hold large amounts of capital as a
function of their business operation. The level of capitalization of the industry and
individual insurers is a central topic of interest in the industry and the focus in recent
years on the efficient allocation of capital, enterprise risk management (ERM) and
operational risk has highlighted the need to better understand the determinants of capital
in the industry. This paper seeks to improve our understanding of the various factors that
influence insurance companies to hold the levels of capital that they do. We briefly
review some of the implicit determinants of capital incorporated into cost of capital and
capital allocation models and explore what are the relevant predictors of capital holdings
for Canadian insurers. We find evidence that insurers hold capital related to their risk
characteristics and for information asymmetry and opportunities for growth purposes.
Further, we find support for the argument that regulatory policies have important
incentive effects for capital holdings.
* Elena Grubisic is Statistical Consultant at Insurance Bureau of Canada (IBC), Policy Development
Dept; tel: (416) 362- 2031, fax: (416) 361-5952, e-mail: egrubisic@ibc.ca
** Darrell Leadbetter is Research Manager at Property and Casualty Insurance Compensation
Corporation (PACICC); tel: (416) 364-8677, 1-888-564-9199, fax: (416) 364-5889, e-mail:
dleadbetter@pacicc.ca
All errors are our own. The views expressed in this paper are those of the authors. No responsibility for
them should be attributed to the Insurance Bureau of Canada or the Property and Casualty Insurance
Compensation Corporation.
1. Introduction
Policyholder confidence in the property and casualty (P&C) insurance industry is
fundamentally based on the belief that insurance contracts will be fulfilled and eligible
claims paid. Other factors may have influence but financial soundness is the core of
public confidence in P&C insurance companies. The most basic and easily understood
definition of financial soundness is expressed by the question “is there enough money?”
Capital is important because it helps answer this question. Capital is the money,
property, and invested assets which collectively represent the wealth of an insurance
company. More explicitly, it is the amount measured by the difference between the best
estimate value of an insurer’s assets and value of an insurer’s obligations when valued at
some predetermined confidence level. For insurance companies, the ability to identify
and manage their capital requirements efficiently is becoming increasingly important in a
highly competitive, international and risk-focused marketplace. Regulatory authorities
are also keenly interested in insurer capital management as capital is a key component of
the solvency supervisory framework. For the purposes of this paper, unless otherwise
stated, capital is defined as the surplus of assets over liabilities.
Capital, whether economic capital (measured by internal capital models of the insurers
risk profile) or regulatory capital (which measures capital according to a particular
accounting or regulatory standard) is the primary measure by which the solvency of
property and casualty (P&C) insurance companies is assessed1. Intuitively, higher capital
levels provide greater confidence that an insurer will be able to pay outstanding
obligations and be resilient to potentially large catastrophic events. A key role of capital
is to act as a buffer against future, unidentified, even relatively remote losses that an
insurance company may incur. An insurer must hold enough capital to cushion both
policyholders and senior lenders against losses, while leaving the insurer able to meet the
needs of its customers. For such solvency purposes, regulatory authorities typically
require insurers to maintain capital commensurate with the amount of risks that they take
and hold enough to weather adverse financial developments.
From a pure solvency perspective then, in some initial period, given an initial portfolio of
insurance exposures, an insurance company would expect to earn some return based on
its expected losses and some level of unknown shocks. In this initial period, an insurer
must decide how much capital to hold. Collignon et al (1999) note that discussion around
capital levels have tended to focus on the capacity to respond to these potential large
unknown (catastrophic) losses. However, holding capital is costly. Despite this,
insurance companies typically hold non-zero capital levels, in many cases beyond that
mandated by regulatory authorities or indicated by catastrophe loss models.
This has at various times led regulatory authorities, insurance analysts and other
commentators to state that the industry was over capitalized. For example, Collignon et
al (1999) estimated that based on risk-adjusted return on capital calculations, the U.S.
1
Regulatory authorities utilize other tools as well, for example, at least as important as capital is the proper
valuation of policy liabilities (technical provisions).
1
industry was 20% to 30% over capitalized. Cummins and Nini (2002) noted that equity
capitalization in the U.S. P&C industry grew by 10% a year during the 1990s. At other
times, such as following the losses of Hurricane Andrew and the 9/11 terrorist attacks,
concern has been expressed that the industry was undercapitalized. In Canada, between
1998 and 2002, claims and associated liabilities grew faster than the industry’s capital
base, weakening the industry’s solvency position, as evidenced by five insurers being
wound-up by supervisory authorities. By 2002, the industry’s capital position had
softened to its lowest point since 1990 (according to a number of measures, including the
insurance risk ratio). Healthy returns and the stabilization of claims costs since 2003
have allowed the industry to begin rebuilding its capital base, but continued to remain
below levels experienced in the late 1990s.
This paper seeks to improve our understanding of the various factors that influence
insurance companies to hold the chosen levels of capital.2 Recent focus on enterprise
risk management (ERM) and operational risk by regulatory authorities in Canada under
the Office of the Superintendent of Financial Institutions (OSFI) supervisory framework,
the Solvency II initiative in Europe and the International Association of Insurance
Supervisors has highlighted the need to better understand the determinants of capital in
the industry. Conceptually insurers do not just hold capital to overcome questions of
potential financial distress, but also because it provides them with financial flexibility. It
would be expected that insurers which are strongly capitalized can take advantage of
growth opportunities.
Using panel data at the individual firm level for primary insurers, extending over the
course of an insurance cycle (1996 – 2005), variables related to financial market risk,
insurance risk and strategic options are tested to identify what factors have an influence
on the capital level of an insurance operation. This is of particular importance for
regulatory authorities, rating agencies, guarantee funds and other stakeholders interested
the solvency and soundness of insurance companies. We add to the literature by using an
expanded data set that allows us to explore issues of signalling and components of risk.
Overall, we find evidence that information asymmetry, financial distress, flexibility to
pursue growth opportunities and the regulatory environment are important determinants
of capital. This paper is organized as follows: section 2 provides a brief overview of the
literature and summarizes some of the findings and analysis. Section 3 provides a
statistical description of the data and methodology. The subsequent sections present the
models and end with a brief discussion of the results.
2. Literature Review
A company’s total capital can be broadly defined as the sum of three components:
operational capital, risk capital and signalling or strategic capital. Operational capital is
minimum capital required to facilitate cash flow and maintain sufficient liquidity to
2
Insurers also have to select their capital structure and the two questions are related. However, we do not
explicitly explore this aspect of insurer capitalization but recognize that there is an extensive literature on
capital allocation and budgeting. See Cummins (2000) for a review of capital allocation in the insurance
industry.
2
manage current operational liabilities such as salaries, leases and IT maintenance. Risk
capital is the additional capital a firm requires to cover the financial consequences of its
business risks. In some cases it may be more precisely defined as the capital needed to
keep the probability of ruin below a predefined level. Signalling/strategic capital is the
further capital required to overcome information asymmetries and reassure external
stakeholders of the firm’s soundness and capacity to survive catastrophic shocks or
pursue other strategic goals such as market share.
P&C insurance is a contingent contract where the policyholder pays up front for a
promise to pay on the occurrence of some future unexpected event. The demand for
insurance from a particular insurance company is therefore sensitive to that institution’s
solvency risk.
Figure 1:
Insurance Company Capital
# simulations
Risk capital
Signalling/strategic
capital
Operational
capital
99% of scenarios
Probability of ruin
X % of scenarios
capital ($)
The use of option pricing, marginal capital allocation and other techniques in identifying
insolvency risk and the level of risk capital is a well established literature. Articles by
Merton and Perold (1993), Cummins and Sommer (1996), Cummins (2000), Myers and
Read (2001) and Sherris (2006) have contributed to a more risk focused approach to
capital budgeting and allocation.
While holding capital increases confidence in the insurer’s solvency and increases
flexibility to pursue strategic goals, capital is costly for several reasons. Perold (2005)
and Jensen (1986) outline the agency and information costs attached to risk capital in any
financial institution. Insurance companies in particular have relatively liquid balance
sheets that can experience substantial change in size and risk over a short time period
(Merton and Perold, 1993). These are changes that policyholders, regulatory authorities
and investors cannot easily monitor.
Additionally, the business of insuring risks is not easily understood by policyholders and
external stakeholders, creating information asymmetry between insurers and
investors/policyholders. Pottier and Sommer (2006) note that opaqueness of the industry
is such that even rating agencies frequently disagree on the financial strength of an
3
insurance company, producing different ratings more than 77% of the time. Further,
Morgan (2002) found that insurance was the most opaque of all industries, including
banking. In such an environment, it would be expected that insurance companies will
generally experience high “agency” and “information” costs in raising equity capital.3
Further, access to capital may be limited for many insurers because they have few
linkages to capital markets (for example farm mutuals) and transaction costs may be too
high. This may be particularly true for Canada where there are few large players and
fewer companies that are public.4 For example in Canada during period between 2000
and 2005 only 2.7% of the capital growth in the industry came directly from capital
markets. The majority of new capital in the industry has been generated from retained
earnings, with the remainder from parent companies at the group level. Cummins and
Nini (2002) found that retained earnings accounted for one third of equity capital growth
in the U.S. Comparatively, retained earnings typically account for two-thirds of capital
growth in Canada.
A few studies have explored the levels of capital holdings by P&C insurance companies.
Cummins and Nini (2002) tested whether insurers held capital against financial distress,
agency costs, asymmetric information, product market interaction and regulation, finding
evidence that insurers hold capital against potential financial distress, agency costs and
asymmetric information. Carayannopoulos and Kelly (2004) reviewed whether capital
levels of Canadian P&C insurers were influenced in a similar fashion as those in the
United States. They concluded that the majority of factors found to be important
predictors of capital in the U.S. (reinsurance utilization, product concentration, earnings
volatility) offered little explanatory power for why insurers hold capital above the
minimum regulatory requirements in Canada.
Because only the level of available capital (as a proxy for the solvency of an insurance
company) is observable, the appropriate level of capital is an important consideration for
regulators and insurers. As a result a number of approaches have developed around the
issue of capital management. Various value at risk (VaR) and dynamic financial analysis
(DFA) approaches seek to identify the appropriate level of capitalization for an insurance
company. Noting that capital costly, a large literature – Shimpi (2002), Perold (2005),
Cummins & Phillips (2005) – has focused on the cost of capital.
Implicitly, these capital allocation models incorporate various determinants of capital into
their formulations. The most familiar, the Capital Asset Pricing Model (CAPM) applies
market risk in its capital allocations. The approach by Cummins and Phillips (2005) – a
CAPM plus model – utilizes market risk, financial distress risk and company size (a
proxy for access to markets) as determinants of the cost of capital.
3
In addition, the tax system imposes another cost on capital as investment income is taxed at the corporate
level and then again when it is realized by the corporation’s shareholders.
4
Excluding government monopoly insurers, the ten largest insurers had less than 40% of the market share
and only eight had direct written premium in excess of (CDN) $1 billion (average $1.5 billion) in 2005.
The industry wrote (CDN) $36 billion in DWP that year.
4
Table 1 compares the various capital allocation and budgeting approaches and the
determinants of capital that they incorporate. As can be seen, these approaches are
oriented toward the traditional trade-off theory of corporate finance which suggests that
companies balance the costs of holding capital with the benefits of reduced risk of
insolvency. The pecking order theory, which suggests that companies prefer financial
flexibility is largely ignored by these models (Hovakimian et al, 2002; Myers & Maljuf,
1984).
Table 1: Comparison of Capital Allocation/Budgeting Models
CAPM
FamaMarginal Risk
French
capital
Adjusted
3/Full
allocation Return on
value
Capital
beta
(RAROC)
Approach
analysis of
CAPM
insolvency insolvency
correlations plus
put option put option
between
approach
approach
entity &
the market
Risk Components
Market risk Yes
Yes
Yes
Yes
Insolvency
No
Yes
Yes
Yes
risk
Operational No
No
No
No*
risk
Comments
Value at
Risk
(VaR)
Dynamic
Financial
Analysis
Regulatory
Risk Based
Tests
probability
of default
probability
of default
fixed ratios
applied to
selected
accounting
positions
No*
Yes
Yes
Yes
Yes
Yes
No*
No*
No*
entity wide,
relies on
market data
entity
applied by adjusts risk based on
can include
does not
wide but line of
based on
volatilities. either
necessarily
can be
business
correlations Not a first
deterministic capture
done by
between
principles
or stochastic economic
line of
lines of
based
modeling
role of
business,
business
approach
approaches
capital.
relies on
market
data
* these models have variations that incorporate operational risk, which is typically defined as investment risk,
which we have defined as market risk. Nevertheless, we believe the capacity for operational risk as currently
being discussed in the ERM literature exists.
3. Data and Methodology
The basic data used in this study, covering the period 1996 to 2005, was obtained from
MSA Research Inc, which publishes a database of insurance company financial
regulatory filings. The database covers all federally regulated P&C insurance companies
and a large number of provincially regulated companies. The database covers an
estimated 95% of the direct premium written by private insurance companies in Canada.
Reciprocal exchanges, reinsurers and government insurers, which operate under different
governance and capital requirements were excluded from the data. The analysis is
conducted at the level of the legal entity. It is recognized that many companies manage
capital at the group level. However, in practice, solvency and regulatory capital
5
requirements are assessed at the legal entity level. We include a dummy variable to
account for and test whether the group is an important element of in determining capital
levels for individual insurers. We define group as a corporate group structure in Canada.
Many foreign owned insurers are stand alone in Canada but may be part of an
international group.
The core analysis uses levels of capital as the dependent variable. As data on minimum
capital requirements was not available for the majority of insurers, total capital rather
than a measure of excess capital was used. As minimum capital requirements differ by
supervisory authority, the sample was further divided into federally supervised and
provincially supervised insurers. As all insurance companies were subject to provincial
market conduct supervision, the primary regulatory difference would be in the solvency
requirements. Splitting the sample allows for the comparison and inference about the
impact of solvency supervision on capital levels. While each province has its own
solvency regime, there are too few provincial insurers to meaningfully test each regime.
Fortunately, the provincial solvency regimes were all based on a similar statutory
foundation and were much more similar to each other than the federal solvency system
over the period of the study. Since 1990 the federal regulatory regime has been more
risk-based, critically permitting greater flexibility for the regulator to adjust minimum
capital requirements based on their risk assessment. A majority of provinces required
only a minimum dollar threshold of capital (either $1 million or $3 million). More
recently, provincial and federal solvency standards have begun to harmonize, particularly
in the larger provinces, with provincial regulatory regimes adopting more risk-based
approaches.
Additional models were run utilizing the Minimum Capital Test (MCT) and the Branch
Adequacy of Assets Test (BAAT) scores as dependent variables. These models were
used primarily for supplemental insights as the data does not extend over a full insurance
cycle. This was considered important as the determinants of capital would be expected to
be influenced by the economic and risk environment. Analysis covering only a profitable
period could potentially provide inappropriate extrapolation to periods of poor
profitability. The MCT/BAAT tests were introduced in 2003 for all federally regulated
insurance companies. In addition, for some companies, it was possible to obtain MCT
and BAAT scores for 2001 and 2002. These regulatory capital tests apply capital factors
to accounting positions on different policy liabilities, reinsurance risks and asset risk.
Carayannopoulos and Kelly (2004) and Cummins and Nini (2002) utilized a number of
characteristics identified as potentially affecting the capitalization of P&C insurance
companies, grouped into the following categories: financial distress, agency costs,
asymmetric information and growth opportunities, and product market interaction. This
paper utilizes a categorization similar to those papers. Specifically we incorporate four
categories of variables that have been identified throughout the literature: financial
distress, product market, agency costs and asymmetric information/strategic
opportunities.
6
Financial distress
The risk profile vector of variables analyses the role that specific risk factors:
profitability, historical earnings volatility, geographic and product concentration,
exposure to rate regulation, earthquake exposure and insolvency environment.
Profitability and the volatility of earnings are directly related to the long term financial
viability of the insurer. Increased volatility is associated with increased risk, therefore we
would expect insurers with less earnings volatility to hold relatively less capital.
Concentration, whether in a specific geographic market or product line, potentially
exposes a company to greater insolvency risk. The broader insolvency environment was
proxied by the cost of insolvency to surviving insurers generated through membership in
the industry guarantee fund, the Property and Casualty Insurance Compensation
Corporation (PACICC). PACICC provided data on the assessments levied on insurance
companies over the period of study. Insurers properly managing their risks and
experiencing insolvency related assessments from the guarantee fund would be expected
to hold capital against this risk.
In addition to the risks that they are exposed to from the underwriting of insurance,
insurance companies also have financial exposure to macroeconomic influences through
the assets that they hold.
The vector of market or macroeconomic influences utilized included the Consumer Price
Index, interest rates (both levels and volatility) and the stock market (Toronto Stock
Exchange). These are significant influences on asset risk, a key regulatory concern in a
financial industry. Most Canadian insurers invest concentrate their investment in bonds,
in 2005, equities comprised less than 9% of insurer assets.
Product market
Commercial and personal insurance product markets have quite different operating
markets. Commercial policyholders, many of which have risk management functions, are
much more cognizant of the financial soundness of their insurer. In addition, commercial
policies typically have larger average loss claim amounts and a longer tail. We include a
ratio of insurer commercial premiums to total premiums written. Given the greater
observed historical volatility in losses and longer tail in commercial lines, this variable
would be expected to be directly related to capitalization.
Agency costs
The agency vector of variables included influences such as company size, reinsurance
utilization, membership in a group, mutual or stock ownership, foreign or Canadian
owned and whether the company was incorporated federally or provincially. Company
size is highly correlated with insolvency risk, both in the insurance industry and the
economy more generally. In the firm survival literature a key empirical regularity is that
survival is highly dependent on firm size and age (Thompson, 2005 & Dunne et al, 1988).
Cummins and Phillips (2005) estimated the risk premium associated with P&C insurers
and found evidence that larger insurers are less sensitive to financial distress than smaller
insurers, although the impact was much smaller than for firms in other industries. Small
stand alone insurers would be expected to hold relatively more capital, while small
7
companies that are part of a larger group may be expected to hold less capital, as capital
would be managed at the group level. A small insurer is defined as an insurer writing
less than $200 million, a medium insurer as a company writing between $200 million and
$750 million.
Foreign owned insurers, with access to international capital would be expected to hold
less capital. An indicator variable with a value of one where a company is foreign owned
is used. Similarly an indicator variable for mutual companies is used. Mutuals are
typically described as being more risk averse, suggesting that they hold more capital than
stock companies.
Canada has a federal charter system for solvency purposes whereby an insurance
company may register with either the federal regulatory or a provincial regulator. A
dummy variable for provincial companies was included as provincial insurers are subject
to different regulatory governance, solvency and capital requirements than federal
insurers.
Information asymmetry and strategic opportunities
The information asymmetry/strategic vector of variables included variables on whether
an insurer had engaged in an acquisition or was observed to have a commitment to
maintaining an A+ rating from A.M. Best (or equivalent from another rating agency).
Data was provided by PACICC on the merger and acquisition history, including portfolio
transfers, of each insurer in the data set. In years that an acquisition occurred, the
variable was set to 1, otherwise zero. Also additional dummy variables for each of the
two years before and two years after an acquisition were created in order to identify
whether insurers acquire capital either in the anticipation of a market move or in the
implementation of an acquisition.
For the rating commitment variable, an index was constructed based on the insurers
revealed commitment to the A+ or greater rating. The variable is an indicator of one if an
insurer has maintained the ratings for a period of more than three years. At the beginning
of the period of study, forty-three companies (12 percent) had a financial strength rating
of A+ or A++. During the period of study, twenty-three were downgraded below A+.
Capital is a consideration in the rating process, so it would be expected that there is a
relationship between capital and financial strength ratings. By focusing on long term
commitment to a rating of A+ (or its equivalent) or greater, we hope to mitigate any
potential collinearity involved and test the hypothesis that signalling is an important by
shifting the focus to the capital committed to maintaining the rating over the course of the
cycle. Many provincial insurance companies are not rated.
The following model is estimated:
Capitalit = α + β Financial distressit + λ Product marketit + γ Agency costsit +δInformation
asymmetry/Strategic it + (disturbance terms)
where the subscripts i and t refer to company and year respectively and α is the intercept.
8
The final model was estimated for 221 companies and 1459 data points. Companies with
up to three years in the market were not included because they have no values for lag
variables.
4. Results
The effect of several macro and micro variables on the capital level that insurance
companies hold is analyzed on an individual level with a panel data model. The period
from 1996 to 2005 was considered. Three models were estimated, a general model, one
for OSFI regulated companies only and one for provincially regulated companies, the last
two from 1999 to 2005.
An additional set of three models was also estimated, in this case the dependent variables
were capital level and MCT/BAAT value, ie we have six models. As MCT is generally
defined since 2003 models were estimated for the 2001-2005 period for companies that
have values for BAAT.
The three core models are presented in the table below and the additional models in
Appendix D.
Table 2: Random Effects models
All
companies
.
Financial distress
CPI
interest rate volatility
TSX volatility
earnings (ROE)
earnings (ROE)-1
ROE volatility
exposure to rate regulation
earthquake exposure
geographic concentration
product concentration
guarantee fund assessments
Product market
Commercial writings
Agency costs
foreign owned
mutual company
medium size
small size
group membership
Federal
3.689
114.089
0.008
0.386
0.164
0.095
**
52.214
5.084
*
57.916
0.060
-26.896
-39.421
0.054
**
*
-24.994
-49.328
0.019
*
***
-9.304
15.644
41.832
-242.102
-321.720
24.515
4.454
174.097
0.034
0.391
0.252
0.159
Provincial
*
**
*
***
*
-12.522
*
*
*
*
9
9.331
42.808
-269.139
-360.168
23.244
6.842
40.872
0.118
0.044
0.055
0.132
-44.337
66.502
-18.387
-8.582
-0.088
**
**
**
-14.579
**
*
*
**
101.033
-44.793
-3.095
-17.170
19.970
**
Information asymmetry /Strategic
M&A activity
23.927
*
32.106
*
-25.270
financial rating stability
50.262
*
47.484
*
12.536
**
2
Adjusted R (OLS no
individual or time dum.var.)
0.643
0.665
0.468
Number of Companies
221
177
35
Number of observations
1459
1078
196
federal, provincial, 1999-2005, all companies 1996 - 2005
* significant at the 1% level
** significant at the 5% level
*** significant at the 10% level
Interestingly macroeconomic variables were not found to have much explanatory power
for capital levels in Canadian insurance companies. This is consistent with
Carayannopoulos and Kelly (2004) but contrasts with research on insolvency (A.M. Best
(2004) and Dibra and Leadbetter (2007)) which finds that interest rate volatility has
historically been correlated with insolvency. However, both of those studies reviewed
factors involved in the failure of insurance companies over a longer time period,
including periods of high interest rate volatility. Historically low levels of interest rate
volatility (and levels) during the period of this study may account for the lack of
significance. Interest rate volatility and inflation (CPI) were significant for the risk based
capital tests.
The risk profile of the P&C company does appear to influence capital. In particular
profitability appears to be an important explanatory variable. Despite its significance, the
coefficients of the profitability variables were typically small in all the models. This may
reflect the incremental effect that profitability has on capital – any given period’s
profitability is small relative to the size of the capital base. The relationship between
profitability and capital growth in the industry – the correlation coefficient is 0.77 – is
illustrated in Figure 2.
Figure 2: Profitability and capital growth in the P&C industry
30%
20%
10%
0%
capital growth
ROE
-10%
-20%
1975
1979
1983
1987
1991
1995
1999
2003
So urce: IB C, with data fro m OSFI
10
As our measure of profitability (ROE) utilizes equity in its calculation, we tested whether
multi-collinearity was a problem with the results. We did not find evidence of multicollinearity and so included the ROE measure of profitability as it better captures the
overall economic performance and risk of an insurer than other measures such as the
underwriting or loss ratios. We test for multicollinearity through variance inflation factors
(VIF) and didn’t find any variables that were significant (VIF>10).
Rate regulation may indicate a government with a propensity to intervene in the market,
typically to suppress prices, thereby increasing the risk to solvency. Given that
inadequate pricing has been found to be the leading cause of insolvency in Canada, the
U.S. and Europe, rate regulation may therefore increase financial distress risk (A.M. Best
(2004) and Dibra and Leadbetter (2007)). Exposure to rate regulation appears to provide
some explanatory power for capital levels in the industry. The coefficients were
significant for all three models. The sign on the coefficient is positive for federal
companies. For provincial companies, the sign is negative, indicating that federal
companies hold more capital for liabilities subject to rate regulation and provincial
companies hold less.
The provincial results are consistent with that of Klein et al (2002) which find that
insurers commit less capital to operations subject to strict price regulation, increasing the
risk of default and reducing the quality of the insurance contract. Because the Canadian
federal solvency system is separate from the provincial market conduct and solvency
systems, a hypothesis is that the federal solvency regulator intervenes to require
additional capital for business subject to rate regulation. This is consistent with
anecdotal evidence reported by insurance companies and concerns expressed by the
federal regulator in its annual reports. As companies are not permitted by statute to reveal
their risk assessments by the regulator we were unable to directly test this hypothesis.
That more than four fifths of the premium subject to rate regulation is federally
supervised allows the positive capital effects to dominate the industry sample.
The economic relevance of rate regulation is also suggested by the size of the coefficient.
We estimate that the impact of rate regulation (increased capital holdings by federal
insurers and decreased capital holdings by provincial insurers) to be $237 million in
2005. This is sufficient capital to underwrite an additional 59,000 vehicles, or equivalent
to the combined Ontario and Alberta residual markets in 2005.5
The earthquake exposure and guarantee fund assessment variables are primarily designed
to provide information on whether external insurance market risks have an effect on
capital decisions. Exposure to earthquake risks was not found to be significant for
federal companies. However, the coefficient was significant, positive and economically
material for provincial companies. This suggests that earthquake exposure may have
some importance for geographically concentrated provincial companies that is not
5
Assuming a claims reserve of $4,000 per written vehicle (the average claim per written vehicle in Ontario
in 2005). If $10,000 were reserved, then potentially up to 24,000 additional vehicles, 65.1% of the Ontario
residual market, could potentially be underwritten if all the freed up capital was applied to underwriting.
Source for residual market data: Facility Association market share reports and Outlook Report.
11
captured by the geographic concentration variable, likely because earthquake exposure is
specific only to British Columbia and Quebec. Guarantee fund assessments by PACICC
were not found to be significant for federal insurers, although when financial strength
stability is removed from the model it does become significant at the 10% level for the
sample as a whole and for federal insurers. This provides some support for the
hypothesis that insurers who have historically been assessed for the insolvency of other
insurers maintain capital for such events. Guarantee fund assessments for provincial
companies were significant but not economically material. One dollar in assessment
reduces capital by nine cents. Overall, the results are consistent with the hypothesis that
provincial companies are more dependent on retained earnings than other insurers. Given
the relatively small number and size of insolvencies in Canada (the largest would have
been $80 million in 2005 dollars) these results suggest the potential for contagion if
insolvencies were more frequent and severe.
Reinsurance was not significant in any model, perhaps because it is a capital allocation
tool rather than a determinant of capital, and was dropped from the specification.
Similarly, the ratio of commercial writings to total premium was not significant in any
model. This contrasts with Cummins and Nini (2002) and may reflect the lower exposure
to catastrophe losses and litigation in the Canadian marketplace.
Geographic and product concentration were significant but contrary to expectation the
sign on the coefficients were negative. Therefore the results do not appear to support the
hypothesis that insurers which diversify across geography and product lines do not
maintain less capital. These results are consistent with that of Cummins and Nini (2002)
and Carayannopoulos and Kelly (2004). Cummins and Nini (2002) hypothesize that
firms that operate in more jurisdictions and lines of business may be dealing with larger
and more complex risks and/or more capital is required for the operational risks of larger
organizations.
We analyzed the correlations between expense data from MSA Research and the
geographic and product concentration measures for 2005 to identify what might be
influencing this result. We found that general expenses had a moderate negative
correlation with both concentration measures (correlation coefficients of -.24 and -.36 for
geographic and product concentration respectively), particularly with regard to
professional expenses. Therefore with diversification, general expenses increase. Partial
correlations between concentration measures and the pure loss ratio were low and
positive (0.004 and 0.176 respectively). Further, partial correlations between geographic
and product concentration (0.34) suggest that there are benefits to diversification,
particularly product diversification. This may be in part due to the dominance of the
central Canadian markets. In 2005 Ontario (43.5%) and Quebec (17.1%) combined
represent 61% of the Canadian insurance market, making it difficult to achieve
geographic diversification. While the relationships need to be explored further (but are
beyond the scope of this paper), there is evidence that diversification has a positive effect
on underwriting risks but may increase the need for operational capital.
As expected, mutual insurance companies and large insurance companies, hold more
capital than smaller insurance companies. This was robust through the core and
12
supplementary models. This suggests that these company characteristics are associated
with both levels and risk based capital ratios.
Members of a corporate group also have access to more capital. At first glance, this is
counterintuitive as most anecdotal evidence suggests that group members would hold
proportionally less capital than stand alone insurers. However, as the analysis extends
over the course of an entire underwriting cycle, it should be interpreted as groups have
more capital over the full length of an underwriting cycle rather than in any given year.
During the cycle covered in the period of analysis, the industry experienced its lowest
level of profitability on record during 2000 and 2001 with return on equity of 2.6% and
1.7% respectively. Further analysis of the data shows that capital levels for stand alone
companies fell during the weak part of the cycle and that capital for companies in
corporate groups was stable or rose modestly. This suggests support for the concept that a
corporate group structure increases capital stability. As can be seen from the additional
models outlined in the appendix, the sign for the risk based tests (MCT/BAAT) is
negative, indicating that while group membership may stabilize capital levels, groups
may hold proportionately less capital than stand alone insurers.
Foreign ownership does not appear to be a significant explanatory variable for federal
insurers but is significant and economically material for provincial insurers. However
while not reported, we did find that when the model is applied only to Canadian
incorporated, and branch companies are excluded from the sample, foreign ownership is
significant at the 10% level. Among provincial companies, all foreign owned companies
are members of groups. The foreign ownership variable may therefore be picking up the
stabilization effect for provincial companies that the group variable does for federal
companies.
Among the information asymmetry and strategic opportunity variables, both M&A
activity and financial strength rating stability are significant and economically material
for federal companies but was not significant for provincial companies. A possible
hypothesis may be that provincial insurers, which typically are regional or niche insurers,
are closer to their market and rely more on informal networks. Very few provincial
insurers are interactively rated. Larger national insurers, underwriting on a national basis
may have fewer informal linkages, may rely more on signalling to indicate their quality.
Other than the size variables, financial strength stability is the most economically
material variable for federal insurers. Combined we estimate that they account for $2.8
billion in industry capitalization, or roughly 45% of the excess capitalization above
regulatory requirements in the industry6. An interpretation might be that signalling and
strategic activities are important explanatory components of why P&C insurance
companies hold the capital levels they do.
6
Excess capital was defined as capital above 180% of the minimum required capital. Insurance companies
are required by the federal regulator to remain above the supervisory target of 150%. In addition,
companies are required to maintain an additional buffer for operational, cat and other risks not explicitly
accounted for in the risk based tests (asset and policy risks).
13
5. Conclusions
This paper investigates the use of capital in the Canadian property and casualty insurance
industry. The investigation is motivated by the growing importance to understanding the
determinants of capital in an ERM world. We focus on capital levels rather than capital
structure since there is already a well developed literature on this topic and levels is a
large part of the solvency dialogue. In addition, there is very little observable diversity in
capital structure among Canadian insurers. Nearly 80% of invested capital in the
Canadian industry is in the form of government bonds and the granularity of the data is
insufficient to separate key characteristics such as duration. Reinsurance, another
important component of capital, was not found to be significant as a determinant of
capital levels.
The primary source of capital growth in the Canadian industry over the decade of study
was retained earnings. We find evidence that profitability has a robust but incremental
impact on the long run implications of capital. This supports the theoretical and
empirical evidence from the U.S. that insurers build up their capital in periods of
profitability as a hedge against the downturn of the underwriting cycle.
Numerous studies and commentators have suggested that the insurance industry holds
capital above that strictly necessary for protection against financial distress. We find that
signalling and strategic reasons can explain a large proportion of what might be termed
excess capital. The significance of signalling financial soundness for insurance
companies supports the hypothesis that the information asymmetry and opaqueness of the
insurance industry identified by Pottier and Sommer (2006) and Morgan (2002) are an
important determinant of capital. In addition, the importance of capital for growth
opportunities, evidenced by mergers and acquisitions, suggests that strategic
opportunities are an important capital consideration.
In addition, we find evidence that solvency regulation of insurance companies can have
capital implications. Differences in solvency regulation between provincial and federal
insurance companies, as well as branch and Canadian incorporated companies appear to
affect the determinants of capital. Given the data limitations we were unable to test
impact of different minimum capital requirements but the results suggest that there are
differences between federal (risk based) and provincial (fixed dollar requirements for
most companies during the period of study) companies. In addition, rate regulation seems
to be particularly important in a federal charter system for solvency regulation, with
provincial insurers reducing capital in the face of price regulation and federal insurers
increasing capital to buffer against adverse development that may not be able to be priced
into the product. This interaction between solvency regulators could be expected to have
important market conduct and availability implications. More research is needed to
establish the extent and strength of these interactions.
Many of the factors found to be determinants of capital are not incorporated as
components in the various capital allocation and budgeting models. This highlights the
importance of ERM in complementing the traditional focus on capital models,
particularly given that operational risks are difficult to quantify, in the solvency process.
14
Further, regulators relying on such models in the establishment of approved returns on
capital as part of the rate setting process should be aware that such models which focus
on trade-off theory may not address operational risk nor account for strategic and growth
opportunities recognized in the pecking order theory of corporate finance.
15
REFERENCES
A.M. Best Company, (2004), “Best’s Insolvency Study/Property Casualty U.S. Insurers
1969-2002”, (Oldwick, NJ: A.M. Best Company).
Carayannopoulos, Peter and Mary Kelly (2004). Determinants of Capital Holdings:
Evidence from the Canadian Property/Casualty Insurance Industry. Journal of Insurance
Regulation, Winter 2004, 23:2 45-65
Collignon, Oliver; Koyluglu, H. Ugur; Nakada, Peter and Shah Hemant, (1999). P&C
RAROC: A Catalyst for Improved Capital Management in the Property and Casualty
Insurance Industry, Journal of Risk Finance, Fall 1999.
Cummins, J. David (2000). Allocation of Capital in the Insurance Industry, Risk
Management and Insurance Review, Spring 2000, 3: 1 7-28.
Cummins, David J., and G.P. Nini (2002). Optimal Capital Utilization by Financial
Firms: Evidence from the Property-Liability Insurance Industry, Journal of Financial
Services Research, 21:15 – 23
Cummins, David J., and Richard D. Phillips, (2005). Estimating the Cost of Equity
Capital for Property-Liability Insurers. Journal of Risk & Insurance, 72:3 441 – 478
Cummins, David J., and D.W. Sommer (1996). Capital and Risk in the PropertyLiability Insurance Markets, Journal of Banking and Finance, 20: 1069 – 1092.
Dibra, Suela and Darrell Leadbetter (2007). “Why Insurers Fail: The dynamics of
property and casualty insurance company failure in Canada”, working paper, Property
and Casualty Insurance Compensation Corporation of Canada.
Dunne, Timothy, Mark J. Roberts and Larry Samuelson (1988). Patterns of Firm Entry
and Exit in U.S. Manufacturing Industries, RAND Journal of Economics, 19: 495-515.
Hovakimian, Armen; Opler, Tim; Titman, Sheridan (2002). The Capital Structure
Choice: New Evidence for a Dynamic Tradeoff Model. Journal of Applied Corporate
Finance, Spring 2002, 15:1
Jensen, Michael C (1986). Agency Costs of Free Cash Flow, Corporate Finance and
Takeovers, American Economic Review, 76:2 323 – 339.
Klein, Robert; Phillips, Richard and Wenyan Shiu (2002). The Capital Structure of Firms
Subject to Price Regulation: Evidence from the Insurance Industry, Journal of Financial
Services Research, 21:1/2 79 – 100.
16
Merton, Robert C., and Andre Perold (1993). Theory of Risk Capital in Financial Firms.
Journal of Applied Corporate Finance, 6:3 16 – 33.
Morgan, Donald P., (2002). Rating Banks: Risk and Uncertainty in an Opaque Industry,
American Economic Review, 92:4 874 – 888.
Myers, S.C and N.S. Maljuf (1984). Corporate Financing and Investment Decisions
when Firms Have Information that Investors Do Not Have. Journal of Financial
Economics, 13: 187 – 221.
Myers, S.C. and James A. Read (2001). Capital Allocation for Insurance Companies,
Journal of Risk and Insurance, 68: 4 545 – 580.
Perold, Andre F., (2005). Capital Allocation in Financial Firms. Journal of Applied
Corporate Finance, Summer 2005, 17:3
Pottier, Steven W., and David W. Sommer (2006). Opaqueness in the Insurance Industry:
Why are Some Insurers Harder to Evaluate than Others?, Risk Management and
Insurance Review, 9:2 149 – 163.
Sherris, Michael (2006). Solvency, Capital Allocation, and Fair Rate of Return in
Insurance, Journal of Risk and Insurance, 73:1 71 – 96.
Shimpi, Prakash, (2002). “Integrating Risk Management and Capital Management,”
Journal of Applied Corporate Finance, Winter 2002, 14:4
Thompson, Peter (2005). Selection and Firm Survival: Evidence from the Shipbuilding
Industry, 1825 – 1914. Review of Economics and Statistics, 87:26 – 36.
17
APPENDIX A
VARIABLES DEFINITION
Table 3: Variable Definitions
Definition
Financial distress
Inflation
Expected
sign
Comments
CPI
Positive
Annual standard deviation
of interest rates
Annual standard deviation
of TSX market value
Return on equity
Moving average of
standard deviation of
ROE
Proportion of DWP
exposed to prior approval
regulation
Proportion of DWP
exposed to earthquake
risk in B.C. & Quebec
Geographic Herfindahl
index
Positive
Greater uncertainty should require a
greater buffer
See above
Positive
See above
Positive
Positive
Profitability generates retained earnings
Greater uncertainty should require a
greater buffer
Negative
Constraints on return expected to reduce
capital allocated
Positive
Capital for addressing potential cat losses
Uncertain
Product concentration
Product Herfindahl index
Uncertain
Insolvency experience
Guarantee fund
assessments incurred by
the company
Reinsurance as a
proportion of total assets
Positive
Increased risk concentration suggests
more capital but if regional companies are
meant to protect group then expect less
capital
Increased risk concentration suggests
more capital but if regional companies are
meant to protect group then expect less
capital
Increased exposure to insolvency should
result in companies recognizing the risk
and holding capital
Reinsurance acts as a substitute or
supplemental form of capital.
Interest rate volatility
Stock market volatility
(TSX)
Earnings (ROE)
ROE volatility
Exposure to rate
regulation
Earthquake exposure
Geographic concentration
Reinsurance
Product market
Commercial writings
Agency costs
Foreign owned
Mutual company
Medium size
Small size
Group member
Negative
Commercial writings as
proportion of DWP
Dummy variable based on
ultimate ownership
Negative
Dummy variable
Positive
DWP in range of $50
million - $600 million
DWP < $50 million
Dummy variable
Positive
Information asymmetry/Strategic
M&A activity Dummy variable on
business acquisition event
Financial strength rating Index of A+ or A++
rating stability
Expectation that international groups
allocate capital efficiently at group level
rather than entity level
Lower risk tolerance among participating
policyholders
Access to capital markets costly
Positive
Negative
Limited/no access to capital markets
If capital is held at the holding company
level and allocated as required
Positive
Positive in prior years as capital built up
but potentially negative in transition
Insurers signaling solvency should hold
more capital.
Positive
18
APPENDIX B
SUMMARY STATISTICS
Table 4. Summary Statistics
Std. Dev.
Mean
Dependent Variables
Capital (000000’s)
MAT_MCT (000’s)*
Financial distress
CPI
interest rate volatility (00’s)
TSX volatility
earnings (ROE)
ROE volatility
exposure to rate regulation
earthquake exposure
geographic concentration
product concentration
guarantee fund assessments
(000000’s)
Min.
Max.
110.497
175.680
0.000
1522.004
335.016
231.869
106.000
999.000
117.866
6.181
108.633
0.335
0.091
0.243
0.492
616.561
146.554
290.908
800.871
6.931
18.739
-246.400
59.900
5.604
13.299
-8.143
171.598
0.169
0.266
0.000
1.000
0.276
0.260
0.000
1.000
0.492
0.284
0.000
1.000
0.560
0.263
0.000
1.000
14.199
58.272
0.000
704.435
0.262
0.331
0.000
1.000
0.672
0.470
0.000
1.000
0.171
0.377
0.000
1.000
0.160
0.367
0.000
1.000
0.795
0.404
0.000
1.000
0.540
0.499
0.000
1.000
Information asymmetry/Strategic
M&A activity
0.032
financial rating stability
0.046
0.176
0.000
1.000
0.209
0.000
1.000
Product market
Commercial writings
Agency costs
foreign owned
mutual company
medium size
small size
group membership
1996-2005 N: 1462, *2001-2005 N: 432
19
127.342
APPENDIX C
CORRELATION MATRIX
Table 5. Correlation Matrix
(1)
1.000
0.126
-0.074
-0.056
0.126
0.102
-0.066
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
(25)
(26)
Variable
Capital
CPI
interest rate volatility
TSX volatility
earnings (ROE)
earnings (ROE)-1
ROE volatility
exposure to rate
regulation
earthquake exposure
geographic
concentration
product concentration
guarantee fund
assessments
foreign owned
mutual company
medium size
small size
group membership
M&A activity
M&A activity-1
M&A activity-2
M&A activity+1
M&A activity+2
financial rating stability
Lloyds
SwissRe
Comm. Writings
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
(25)
(26)
Variable
medium size
small size
group membership
M&A activity
M&A activity-1
M&A activity-2
M&A activity+1
M&A activity+2
financial rating stability
Lloyds
SwissRe
Comm. Writings
(15)
1.000
-0.859
0.291
0.090
0.098
0.088
0.085
0.058
0.038
0.069
-0.051
0.023
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
1.000
-0.444
-0.402
0.188
0.102
-0.074
1.000
-0.004
-0.176
-0.072
0.030
1.000
-0.121
-0.036
0.088
1.000
0.415
-0.273
1.000
-0.454
1.000
0.160
-0.053
0.078
0.004
-0.048
0.014
-0.015
0.006
-0.040
0.095
-0.075
0.103
0.085
-0.064
1.000
-0.363
1.000
-0.141
-0.137
0.054
0.048
-0.021
-0.014
-0.024
-0.015
-0.037
0.009
-0.061
-0.027
0.014
0.055
0.119
0.168
-0.045
-0.086
1.000
0.118
1.000
0.397
0.002
0.078
0.286
-0.618
0.215
0.209
0.160
0.119
0.243
0.258
0.017
0.064
-0.009
0.002
0.174
-0.089
0.013
0.092
-0.138
0.059
-0.012
-0.060
0.079
0.016
0.030
0.241
-0.001
-0.022
0.028
-0.160
0.034
-0.001
-0.047
0.076
0.018
0.047
0.014
0.074
-0.026
-0.002
-0.192
0.000
0.012
-0.014
-0.111
0.022
-0.003
-0.049
0.074
-0.017
-0.026
0.040
0.035
0.008
-0.032
-0.155
0.000
0.009
-0.013
0.055
-0.001
-0.015
0.073
-0.095
0.039
0.002
0.026
-0.007
0.013
0.018
0.140
0.012
-0.020
0.031
-0.051
0.037
0.002
0.053
-0.068
0.018
0.026
0.012
0.000
0.016
0.018
0.097
0.031
0.045
0.048
-0.011
0.023
-0.058
-0.038
0.039
0.066
-0.025
-0.016
-0.012
-0.016
0.007
-0.032
0.084
0.082
-0.056
0.119
-0.197
0.080
0.127
-0.175
0.257
0.052
0.033
0.018
0.050
0.054
-0.034
-0.030
-0.075
-0.267
-0.040
0.065
-0.100
0.055
-0.012
0.077
0.006
0.026
0.029
0.001
-0.019
0.016
0.034
0.012
0.198
-0.034
-0.318
0.189
-0.013
0.057
-0.076
-0.046
-0.039
-0.039
-0.057
-0.066
-0.105
-0.071
-0.081
-0.141
-0.023
-0.001
0.048
0.057
0.085
-0.013
-0.009
-0.020
-0.032
-0.021
-0.034
0.017
-0.054
-0.080
0.069
(16)
1.000
-0.316
-0.176
-0.143
-0.109
-0.197
-0.195
-0.083
-0.146
0.060
-0.033
(17)
1.000
0.114
0.096
0.091
0.111
0.098
0.058
-0.080
0.097
-0.035
(18)
1.000
0.160
0.026
0.181
0.041
-0.003
-0.014
0.045
-0.002
(19)
1.000
0.146
0.062
0.041
-0.037
-0.012
0.052
0.000
(20)
1.000
0.024
0.052
-0.033
-0.011
0.063
0.004
(21)
1.000
0.181
0.032
-0.014
0.076
-0.003
(22)
1.000
0.053
-0.014
0.045
-0.002
(23)
1.000
-0.016
-0.026
0.143
(24)
1.000
-0.009
0.078
(25)
1.000
-0.093
(12)
1.000
-0.027
0.018
0.147
-0.375
0.137
0.080
0.056
-0.007
0.147
0.150
0.126
0.069
-0.029
0.016
(26)
1.000
(13)
1.000
-0.267
-0.045
0.064
-0.010
0.003
0.010
0.005
0.009
0.028
0.132
0.052
0.082
0.103
(14)
1.000
-0.045
0.019
-0.105
-0.031
-0.009
-0.018
-0.024
-0.041
0.031
-0.034
-0.053
0.052
APPENDIX D
REGRESSION OUTPUT
Table 6
Dependent Variable : Capital
Explanatory Variables
CPI
interest rate volatility
TSX volatility
earnings (ROE)
earnings (ROE)-1
ROE volatility
exposure to rate regulation
earthquake exposure
geographic concentration
product concentration
Commercial writings
guarantee fund assessments
foreign owned
mutual company
medium size
small size
group membership
M&A activity
M&A activity-1
M&A activity-2
M&A activity+1
M&A activity+2
financial rating stability
Constant
Adjusted R2 (OLS when no
individual or time variables)
All Companies
Federal
Provincial
3.689
(0.043)
114.089
(0.511)
0.008
(0.944)
0.386
(0.000)
0.162
(0.080)
0.095
(0.528)
52.214
(0.000)
5.084
(0.699)
-26.896
(0.018)
-39.421
(0.001)
-9.304
(0.395)
0.054
(0.103)
4.454
(0.253)
174.097
(0.514)
0.034
(0.787)
0.391
(0.001)
0.252
(0.044)
0.159
(0.388)
57.916
(0.001)
0.060
(0.997)
-24.994
(0.069)
-49.328
(0.000)
-12.522
(0.350)
0.019
(0.608)
6.842
(0.172)
40.872
(0.886)
0.118
(0.285)
0.044
(0.701)
0.055
(0.588)
0.132
(0.512)
-44.337
(0.041)
66.502
(0.014)
-18.387
(0.520)
-8.582
(0.840)
-14.579
(0.424)
-0.088
(0.050)
15.644
(0.198)
41.832
(0.003)
-242.102
(0.000)
-321.720
(0.000)
24.515
(0.003)
9.331
(0.547)
42.808
(0.011)
-269.139
(0.000)
-360.168
(0.000)
23.244
(0.042)
101.033
(0.020)
-44.793
(0.354)
-3.095
(0.885)
-17.170
(0.462)
19.970
(0.379)
23.927
(0.009)
4.029
(0.683)
5.009
(0.654)
19.313
(0.035)
22.108
(0.019)
50.262
(0.000)
32.106
(0.002)
31.666
(0.009)
13.045
(0.308)
26.711
(0.010)
20.159
(0.054)
47.484
(0.000)
-25.270
(0.036)
-18.148
(0.224)
-23.865
(0.118)
-28.525
(0.028)
-30.508
(0.040)
12.536
(0.299)
-92.756
(0.749)
-171.389
(0.766)
-861.306
(0.230)
0.643
0.665
0.468
P-value in parenthesis
21
APPENDIX D
REGRESSION OUTPUT
Table 7: Supplementary models – risk-based capital
Dependent Variable
Explanatory Variables
MCT, BAAT comp.
Capital
MCT
MCT companies
Capital
MCT
-10.434
(0.276)
-664.544
(0.093)
0.0249
(0.842)
0.424
(0.010)
0.435
(0.002)
0.269
(0.286)
44.651
(0.028)
-20.253
(0.304)
-47.261
(0.011)
-45.339
(0.019)
1.105
(0.947)
-0.098
(0.002)
41.954
(0.012)
1589.553
(0.021)
0.101
(0.629)
0.316
(0.315)
0.388
(0.154)
-0.450
(0.354)
8.681
(0.826)
64.086
(0.093)
120.399
(0.001)
70.429
(0.060)
-19.086
(0.558)
0.016
(0.797)
-12.251
(0.342)
-682.176
(0.189)
-0.035
(0.836)
0.298
(0.293)
0.180
(0.378)
0.386
(0.312)
45.869
(0.095)
-9.284
(0.743)
-62.804
(0.017)
-65.022
(0.049)
-16.140
(0.568)
-0.0834
(0.029)
55.019
(0.001)
2307.511
(0.001)
-0.302
(0.183)
1.268
(0.001)
0.385
(0.148)
-0.547
(0.276)
-98.486
(0.009)
-13.634
(0.730)
76.317
(0.035)
39.431
(0.395)
8.658
(0.828)
-0.034
(0.495)
27.325
(0.101)
53.282
(0.007)
-272.831
(0.000)
-343.628
(0.000
60.874
(0.000)
132.411
(0.000)
79.105
(0.042)
21.773
(0.538)
73.2572
(0.057)
-185.464
(0.000)
72.446
(0.002)
53.627
(0.049)
-268.720
(0.000)
-333.054
(0.000)
6.875
(0.740)
12.601
(0.714)
39.712
(0.319)
2.996
(0.917)
37.528
(0.249)
-119.268
(0.000)
-13.204
(0.323)
-2.999
(0.907)
-25.561
(0.124)
M&A activity+1
-1.351
(0.941)
-112.827
(0.000)
6.078
(0.624)
28.359
(0.421)
-43.318
(0.374)
-4.895
(0.837)
M&A activity+2
16.805
(0.143)
CPI
interest rate volatility
TSX volatility
earnings (ROE)
earnings (ROE)-1
ROE volatility
exposure to rate regulation
earthquake exposure
geographic concentration
product concentration
Commercial writings
guarantee fund assessments
foreign owned
mutual company
medium size
small size
group membership
M&A activity
M&A activity-1
M&A activity-2
Constant
Adjusted R2 (OLS when no
individual or time variables)
BAAT companies
Capital
MCT
---
---
---
---
--0.304
(0.063)
0.491
(0.001)
0.140
(0.593)
1.101
(0.960)
-51.282
(0.013)
-14.282
(0.403)
-7.647
(0.619)
-10.443
(0.401)
-0.427
(0.000)
---0.699
(0.317)
-0.458
(0.483)
-1.738
(0.122)
397.814
(0.000)
221.058
(0.013)
252.516
(0.001)
64.964
(0.329)
-16.619
(0.759)
-0.078
(0.762)
---
---
57.789
(0.000)
-439.345
(0.000)
-546.813
(0.000)
49.652
(0.000)
14.238
(0.837)
255.552
(0.074)
338.334
(0.012)
-110.899
(0.041)
-21.648
(0.324)
117.474
(0.000)
33.520
(0.701)
-39.390
(0.073)
-97.0356
(0.004)
-0.703
(0.963)
20.759
(0.475)
-56.859
(0.194)
-6.383
(0.749)
306.437
(0.000)
175.926
(0.000)
81.470
(0.000)
38.453
(0.800)
-38.038
(0.840)
-9.363
(0.908)
-5.574
(0.800)
-4.768
(0.736)
-6.277
(0.736)
84.383
(0.000)
10.690
(0.879)
1855.891
(0.147)
-5428.531
(0.015)
2175.861
(0.208)
-6940.903
(0.002)
604.545
(0.000)
29.855
(0.862)
0.67
0.307
0.68
0.304
0.684
0.29
Period: 2001-2005. P-value in parenthesis.
22